Python机器学习课程(代码与教程)

5 月 13 日 专知

作者 | machinelearningmindset 

编译 | Xiaowen

github:

https://github.com/machinelearningmindset/machine-learning-course


目录


  • 简介

  • 目的

  • 机器学习

    • 机器学习基础

    • 监督学习

    • 非监督学习

    • 深度学习


简介


这个项目的目的是提供一个全面但简单的用python完成机器学习的教程。


目的


机器学习作为人工智能的一种工具,是应用最广泛的科学领域之一。大量关于机器学习的文献已经发表。本项目的目的是通过提供一系列使用python的简单而全面的教程来帮助读者学习机器学习。在这个项目中,我们使用许多不同的机器学习框架 (如Scikit-Learning) 构建我们的教程。在本项目中,你将了解:


  • 机器学习的定义

  • 开始和发展趋势

  • 机器学习的分类和子类

  • 机器学习中最常用的算法,以及如何实现这些算法


01

机器学习基础




1. 线性回归

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression  


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/overview/linear-regression.rst


2. 过拟合/欠拟合


代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/overfitting  


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/overview/overfitting.rst


3. 正则化

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/regularization


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/overview/regularization.rst


4. 交叉验证

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/cross-validation


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/overview/crossvalidation.rst


02

监督学习



1. 决策树

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/supervised/DecisionTree/decisiontrees.py


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/supervised/decisiontrees.rst


2. K近邻

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/supervised/KNN/knn.py


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/supervised/knn.rst


3. 朴素贝叶斯

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/supervised/Naive_Bayes


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/supervised/bayes.rst


4. 逻辑回归

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/supervised/Logistic_Regression/logistic_ex1.py


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/supervised/logistic_regression.rst


5. 支持向量机

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/supervised/Linear_SVM/linear_svm.py


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/supervised/linear_SVM.rst


03

无监督学习


1. 聚类

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/unsupervised/Clustering


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/unsupervised/clustering.rst


2. 主成分分析

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/unsupervised/PCA


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/unsupervised/pca.rst


04

深度学习


1. 神经网络概览

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/deep_learning/mlp


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/deep_learning/mlp.rst


2. 卷积神经网络

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/deep_learning/cnn


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/deep_learning/cnn.rst


3. 自编码器

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/deep_learning/autoencoder


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/deep_learning/autoencoder.rst


4. 循环神经网络

代码:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/docs/source/content/deep_learning/autoencoder.rst


教程:

https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/deep_learning/rnn/rnn.ipynb


作者信息:

Creator: Machine Learning Mindset 

Supervisor: Amirsina Torfi 

Developers: Brendan Sherman*, James E Hopkins* , Zac Smith 


-END-

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